Search results for: association-rule-mining

Association Rule Mining

Author : Chengqi Zhang
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Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention. The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.

Rare Association Rule Mining a Systematic Review

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One of the indispensable tasks of data mining is the extraction of significant and meaningful association rules. Whereas the extraction of frequent patterns using association rule mining is an imperative field of research, the idea of generating patterns that do not appear frequently in a database has grabbed the attention of researchers in recent years. The infrequent items or more commonly known as the rare items represent unknown or unpredictable associations and are therefore more interesting than the frequent ones. This study aims to provide a broad systematic review of the area of rare association rule mining. In this paper, a methodical analysis of the rare itemset and rare rule generation techniques in static and dynamic environment is presented. This paper also attempts to feature the current status and future perspectives of rare association rule mining along with some major research challenges.

Data Mining for Association Rules and Sequential Patterns

Author : Jean-Marc Adamo
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Recent advances in data collection, storage technologies, and computing power have made it possible for companies, government agencies and scientific laboratories to keep and manipulate vast amounts of data relating to their activities. This state-of-the-art monograph discusses essential algorithms for sophisticated data mining methods used with large-scale databases, focusing on two key topics: association rules and sequential pattern discovery. This will be an essential book for practitioners and professionals in computer science and computer engineering.

Association Rule Hiding for Data Mining

Author : Aris Gkoulalas-Divanis
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Privacy and security risks arising from the application of different data mining techniques to large institutional data repositories have been solely investigated by a new research domain, the so-called privacy preserving data mining. Association rule hiding is a new technique in data mining, which studies the problem of hiding sensitive association rules from within the data. Association Rule Hiding for Data Mining addresses the problem of "hiding" sensitive association rules, and introduces a number of heuristic solutions. Exact solutions of increased time complexity that have been proposed recently are presented, as well as a number of computationally efficient (parallel) approaches that alleviate time complexity problems, along with a thorough discussion regarding closely related problems (inverse frequent item set mining, data reconstruction approaches, etc.). Unsolved problems, future directions and specific examples are provided throughout this book to help the reader study, assimilate and appreciate the important aspects of this challenging problem. Association Rule Hiding for Data Mining is designed for researchers, professors and advanced-level students in computer science studying privacy preserving data mining, association rule mining, and data mining. This book is also suitable for practitioners working in this industry.

Association Rule Mining

Author : Zhang (Shichao.)
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Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention. The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.

Association Rule Mining Over Multiple Databases

Author : Hima Valli Kona
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Rare Association Rule Mining and Knowledge Discovery Technologies for Infrequent and Critical Event Detection

Author : Koh, Yun Sing
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"This book provides readers with an in-depth compendium of current issues, trends, and technologies in association rule mining"--Provided by publisher.

Neutrosophic Association Rule Mining Algorithm for Big Data Analysis

Author : Mohamed Abdel-Basset
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Big Data is a large-sized and complex dataset, which cannot be managed using traditional data processing tools. Mining process of big data is the ability to extract valuable information from these large datasets.

Algorithms and Applications for Academic Search Recommendation and Quantitative Association Rule Mining

Author : Emmanouil Amolochitis
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Algorithms and Applications for Academic Search, Recommendation and Quantitative Association Rule Mining presents novel algorithms for academic search, recommendation and association rule mining that have been developed and optimized for different commercial as well as academic purpose systems. Along with the design and implementation of algorithms, a major part of the work presented in the book involves the development of new systems both for commercial as well as for academic use. In the first part of the book the author introduces a novel hierarchical heuristic scheme for re-ranking academic publications retrieved from standard digital libraries. The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper's index terms with each other. In order to evaluate the performance of the introduced algorithms, a meta-search engine has been designed and developed that submits user queries to standard digital repositories of academic publications and re-ranks the top-n results using the introduced hierarchical heuristic scheme. In the second part of the book the design of novel recommendation algorithms with application in different types of e-commerce systems are described. The newly introduced algorithms are a part of a developed Movie Recommendation system, the first such system to be commercially deployed in Greece by a major Triple Play services provider. The initial version of the system uses a novel hybrid recommender (user, item and content based) and provides daily recommendations to all active subscribers of the provider (currently more than 30,000). The recommenders that we are presenting are hybrid by nature, using an ensemble configuration of different content, user as well as item-based recommenders in order to provide more accurate recommendation results. The final part of the book presents the design of a quantitative association rule mining algorithm. Quantitative association rules refer to a special type of association rules of the form that antecedent implies consequent consisting of a set of numerical or quantitative attributes. The introduced mining algorithm processes a specific number of user histories in order to generate a set of association rules with a minimally required support and confidence value. The generated rules show strong relationships that exist between the consequent and the antecedent of each rule, representing different items that have been consumed at specific price levels. This research book will be of appeal to researchers, graduate students, professionals, engineers and computer programmers.

Mining Association Rules Events Over Data Streams

Author : Aref Faisal Mourtada
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Data streams have gained considerable attention in data analysis and data mining communities because of the emergence of a new classes of applications, such as monitoring, supply chain execution, sensor networks, oilfield and pipeline operations, financial marketing and health data industries. Telecommunication advancements have provided us with easy access to stream data produced by various applications. Data in streams differ from static data stored in data warehouses or database. Data streams are continuous, arrive at high-speeds and change through time. Traditional data mining algorithms assume presence of data in conventional storage means where data mining is performed centrally with the luxury of accessing the data multiple times, using powerful processors, providing offline output with no time constraints. Such algorithms are not suitable for dynamic data streams. Stream data needs to be mined promptly as it might not be feasible to store such volume of data. In addition, streams reflect live status of the environment generating it, so prompt analysis may provide early detection of faults, delays, performance measurements, trend analysis and other diagnostics. This thesis focuses on developing a data stream association rule mining algorithm among co-occurring events. The proposed algorithm mines association rules over data streams incrementally in a centralized setting. We are interested in association rules that meet a provided minimum confidence threshold and have a lift value greater than 1. We refer to such association rules as strong rules. Experiments on several datasets demonstrate that the proposed algorithms is efficient and effective in extracting association rules from data streams, thus having a faster processing time and better memory management.

Semantic and Association Rule Mining based Knowledge Extension for Reusable Medical Equipment Lifecycle Management

Author : Jong Youl Lee
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For healthcare providers, using Reusable Medical Equipment (RME) has a strength in the cost-efficiency since it can be reused and reprocessed to multiple patients. Hence, estimating the maintenance (i.e., repair) cost during RME lifecycle has been a topic in healthcare domain. However, most of the existing research regarding RME has focused on the prediction without considering the domain knowledge of the cost in healthcare. This aim of the research is to propose the method of knowledge extension based on the post-mining (i.e., Association Rule Mining) interpreted by the domain knowledge (i.e., RME ontology and statistical cost domain knowledge) for RME lifecycle management. This contains finding the frequent rule patterns from the tremendous volumes of decision rules (i.e., Random Forest Rules) of the non-profit hospital's legacy database, which can make the pruned frameworks of each rule pattern linked and interpreted to the proper domain knowledge. The interpreted rule patterns make healthcare providers utilize them in the RME lifecycle management decision making.

DARM

Author :
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Post Mining of Association Rules Techniques for Effective Knowledge Extraction

Author : Zhao, Yanchang
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Provides a systematic collection on post-mining, summarization and presentation of association rules, and new forms of association rules.

Association Rule Mining Based Disease Signatures Discovery

Author : Abdulrahman Ibrahim Alothaim
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The focus of this thesis is discovering disease signatures using association rule mining. A disease signature is the common characteristics that may give the disease patients a distinct identity. In this thesis, we are specifically analyzing the dynamic gaits of Parkinson's patients and healthy controls to identify potential disease signatures. This can be useful as such signatures can be considered potential indicators of the disease. Since there is no preemptive test or marker for Parkinson's disease diagnoses, these types of signatures can help physicians be proactive. Currently a diagnosis of Parkinson's disease is primarily based on a patient's medical history and some neurological exams which include analysis of movement and balance [1]. Our approach can be generalized to any type of gait analysis. We use association rule mining as a building block to identify signatures. We tested our approach on the dynamic gaits of 15 Parkinson patients and 16 healthy controls. We used Coron [2] and Weka [3] systems in our implementation. The data was obtained from PhysioBank [4], a public source for biomedical data. Our results identified significant signatures for Parkinson's disease in approximately 50% of the data. We also identified some overlap in signatures between Parkinson's patients and healthy controls, indicating that gait speed and height may be important factors in discovering signatures. We validated our results using Pearson correlation and found that correlation increases substantially in Parkinson's patients, indicating that the signatures are valid, especially in Parkinson's patients.

A Privacy preserving Framework for Collaborative Association Rule Mining in Cloud

Author : Salha Albehairi
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Collaborative Data Mining facilitates multiple organizations to integrate their datasets and extract useful knowledge from their joint datasets for mutual benefits. The knowledge extracted in this manner is found to be superior to the knowledge extracted locally from a single organization's dataset. With the rapid development of outsourcing, there is a growing interest for organizations to outsource their data mining tasks to a cloud environment to effectively address their economic and performance demands. However, due to privacy concerns and stringent compliance regulations, organizations do not want to share their private datasets neither with the cloud nor with other participating organizations. In this paper, we address the problem of outsourcing association rule mining task to a federated cloud environment in a privacy-preserving manner. Specifically, we propose a privacy-preserving framework that allows a set of users, each with a private dataset, to outsource their encrypted databases and the cloud returns the association rules extracted from the aggregated encrypted databases to the participating users. Our proposed solution ensures the confidentiality of the outsourced data and also minimizes the users' participation during the association rule mining process. Additionally, we show that the proposed solution is secure under the standard semi-honest model and demonstrate its practicality.

Advances in Data Mining

Author : Petra Perner
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This book constitutes the refereed proceedings of the 6th Industrial Conference on Data Mining, ICDM 2006, held in Leipzig, Germany in July 2006. Presents 45 carefully reviewed and revised full papers organized in topical sections on data mining in medicine, Web mining and logfile analysis, theoretical aspects of data mining, data mining in marketing, mining signals and images, and aspects of data mining, and applications such as intrusion detection, and more.

Advances in Data Mining Medical Applications E Commerce Marketing and Theoretical Aspects

Author : Petra Perner
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ICDM / MLDM Medaillie (limited edition) Meissner Porcellan, the “White Gold” of King August the Strongest of Saxonia ICDM 2008 was the eighth event of the Industrial Conference on Data Mining held in Leipzig (www.data-mining-forum.de). For this edition the Program Committee received 116 submissions from 20 countries. After the peer-review process, we accepted 36 high-quality papers for oral presentation, which are included in these proceedings. The topics range from aspects of classification and prediction, clustering, Web mining, data mining in medicine, applications of data mining, time series and frequent pattern mining, and association rule mining. Thirteen papers were selected for poster presentations that are published in the ICDM Poster Proceeding Volume. In conjunction with ICDM there were three workshops focusing on special hot application-oriented topics in data mining. The workshop Data Mining in Life Science DMLS 2008 was held the third time this year and the workshop Data Mining in Marketing DMM 2008 ran for the second time this year. Additionally, we introduced an International Workshop on Case-Based Reasoning for Multimedia Data CBR-MD.

Multi threaded Implementation of Association Rule Mining with Visualization of the Pattern Tree

Author : Eera Gupta
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New Frontiers in Applied Data Mining

Author : Sanjay Chawla
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Asdataminingtechniquesandtoolsmature,theirapplicationdomainsextendto previousuncharteredterritories.The commontheme ofthe workshopsorganized along with the main 2008 Paci?c Asia Conference on Knowledge Discovery and Data Mining (PAKDD) in Osaka, Japan was to extend the application of data mining techniques to new frontiers. Thus the title of the proceedings: “New Frontiers in Application of Data Mining.” For the 2008 program, three workshops were organized. 1. Algorithms for Large-Scale Information Processing (ALSIP). The focus of the workshop was novel algorithms and data structures to deal with p- cessing of very large data sets. 2. Data Mining for Decision Making and Risk Management (DMDRM), which emphasized applications of risk information derived from data mining te- niques on diverse applications ranging from medicine to marketing to chemistry. 3. Interactive Data Mining (IDM), which emphasized the relationship between techniques in data mining and human–computer interaction. In total 38 papers were submitted to the workshops. After consultation with theworkshopChairswhowereaskedto ranktheir submissions,18wereaccepted for publicationin this volume.We hope that the published papers propelfurther interest in the growing ?eld of knowledge discovery in databases (KDD). The paper selection of the industrial track and the workshops was made by the Program Committee of each organization. Upon the paper selection, the book was edited and managed by the volume editors.

Advances in Knowledge Discovery and Data Mining

Author : Takashi Washio
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This book constitutes the refereed proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008, held in Osaka, Japan, in May 2008. The 37 revised long papers, 40 revised full papers, and 36 revised short papers presented together with 1 keynote talk and 4 invited lectures were carefully reviewed and selected from 312 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scientific discovery, data visualization, causal induction, and knowledge-based systems.